Overview

Dataset statistics

Number of variables16
Number of observations800
Missing cells386
Missing cells (%)3.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory133.1 KiB
Average record size in memory170.3 B

Variable types

Numeric11
Categorical4
Boolean1

Alerts

Name_x has a high cardinality: 800 distinct valuesHigh cardinality
# is highly overall correlated with GenerationHigh correlation
Total is highly overall correlated with HP and 6 other fieldsHigh correlation
HP is highly overall correlated with Total and 1 other fieldsHigh correlation
Attack is highly overall correlated with Total and 2 other fieldsHigh correlation
Defense is highly overall correlated with Total and 2 other fieldsHigh correlation
Sp. Atk is highly overall correlated with Total and 2 other fieldsHigh correlation
Sp. Def is highly overall correlated with Total and 2 other fieldsHigh correlation
Speed is highly overall correlated with TotalHigh correlation
Generation is highly overall correlated with #High correlation
Name_y is highly overall correlated with percentage and 2 other fieldsHigh correlation
percentage is highly overall correlated with Name_y and 2 other fieldsHigh correlation
Type 1 is highly overall correlated with Name_y and 2 other fieldsHigh correlation
Legendary is highly overall correlated with Total and 1 other fieldsHigh correlation
Segment is highly overall correlated with Name_y and 2 other fieldsHigh correlation
Legendary is highly imbalanced (59.3%)Imbalance
Segment is highly imbalanced (65.3%)Imbalance
Type 2 has 386 (48.2%) missing valuesMissing
# is uniformly distributedUniform
Name_x is uniformly distributedUniform
Name_x has unique valuesUnique

Reproduction

Analysis started2023-04-08 12:46:15.298403
Analysis finished2023-04-08 12:46:36.076476
Duration20.78 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

#
Real number (ℝ)

HIGH CORRELATION  UNIFORM 

Distinct721
Distinct (%)90.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean362.81375
Minimum1
Maximum721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:36.212115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34.95
Q1184.75
median364.5
Q3539.25
95-th percentile689.05
Maximum721
Range720
Interquartile range (IQR)354.5

Descriptive statistics

Standard deviation208.3438
Coefficient of variation (CV)0.57424449
Kurtosis-1.1657051
Mean362.81375
Median Absolute Deviation (MAD)177.5
Skewness-0.0011225028
Sum290251
Variance43407.138
MonotonicityIncreasing
2023-04-08T15:46:36.386351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
479 6
 
0.8%
386 4
 
0.5%
710 4
 
0.5%
711 4
 
0.5%
646 3
 
0.4%
150 3
 
0.4%
413 3
 
0.4%
6 3
 
0.4%
319 2
 
0.2%
642 2
 
0.2%
Other values (711) 766
95.8%
ValueCountFrequency (%)
1 1
 
0.1%
2 1
 
0.1%
3 2
0.2%
4 1
 
0.1%
5 1
 
0.1%
6 3
0.4%
7 1
 
0.1%
8 1
 
0.1%
9 2
0.2%
10 1
 
0.1%
ValueCountFrequency (%)
721 1
0.1%
720 2
0.2%
719 2
0.2%
718 1
0.1%
717 1
0.1%
716 1
0.1%
715 1
0.1%
714 1
0.1%
713 1
0.1%
712 1
0.1%

Name_x
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct800
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size44.8 KiB
Bulbasaur
 
1
Uxie
 
1
GalladeMega Gallade
 
1
Probopass
 
1
Dusknoir
 
1
Other values (795)
795 

Length

Max length25
Median length23
Mean length8.84125
Min length3

Characters and Unicode

Total characters7073
Distinct characters63
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique800 ?
Unique (%)100.0%

Sample

1st rowBulbasaur
2nd rowIvysaur
3rd rowVenusaur
4th rowVenusaurMega Venusaur
5th rowCharmander

Common Values

ValueCountFrequency (%)
Bulbasaur 1
 
0.1%
Uxie 1
 
0.1%
GalladeMega Gallade 1
 
0.1%
Probopass 1
 
0.1%
Dusknoir 1
 
0.1%
Froslass 1
 
0.1%
Rotom 1
 
0.1%
RotomHeat Rotom 1
 
0.1%
RotomWash Rotom 1
 
0.1%
RotomFrost Rotom 1
 
0.1%
Other values (790) 790
98.8%

Length

2023-04-08T15:46:36.563340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
forme 21
 
2.3%
size 8
 
0.9%
rotom 6
 
0.7%
charizard 3
 
0.3%
cloak 3
 
0.3%
kyurem 3
 
0.3%
mewtwo 3
 
0.3%
swampert 2
 
0.2%
sharpedo 2
 
0.2%
heracross 2
 
0.2%
Other values (796) 846
94.1%

Most occurring characters

ValueCountFrequency (%)
a 682
 
9.6%
e 644
 
9.1%
o 571
 
8.1%
r 509
 
7.2%
i 469
 
6.6%
n 378
 
5.3%
l 371
 
5.2%
t 319
 
4.5%
u 248
 
3.5%
s 220
 
3.1%
Other values (53) 2662
37.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5969
84.4%
Uppercase Letter 994
 
14.1%
Space Separator 99
 
1.4%
Other Punctuation 4
 
0.1%
Decimal Number 3
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 682
11.4%
e 644
10.8%
o 571
 
9.6%
r 509
 
8.5%
i 469
 
7.9%
n 378
 
6.3%
l 371
 
6.2%
t 319
 
5.3%
u 248
 
4.2%
s 220
 
3.7%
Other values (17) 1558
26.1%
Uppercase Letter
ValueCountFrequency (%)
S 137
13.8%
M 126
12.7%
C 65
 
6.5%
G 65
 
6.5%
P 59
 
5.9%
A 55
 
5.5%
F 50
 
5.0%
B 49
 
4.9%
D 48
 
4.8%
L 46
 
4.6%
Other values (16) 294
29.6%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
% 1
25.0%
' 1
25.0%
Decimal Number
ValueCountFrequency (%)
5 1
33.3%
0 1
33.3%
2 1
33.3%
Other Symbol
ValueCountFrequency (%)
♀ 1
50.0%
♂ 1
50.0%
Space Separator
ValueCountFrequency (%)
99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6963
98.4%
Common 110
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 682
 
9.8%
e 644
 
9.2%
o 571
 
8.2%
r 509
 
7.3%
i 469
 
6.7%
n 378
 
5.4%
l 371
 
5.3%
t 319
 
4.6%
u 248
 
3.6%
s 220
 
3.2%
Other values (43) 2552
36.7%
Common
ValueCountFrequency (%)
99
90.0%
- 2
 
1.8%
. 2
 
1.8%
5 1
 
0.9%
0 1
 
0.9%
% 1
 
0.9%
♀ 1
 
0.9%
♂ 1
 
0.9%
' 1
 
0.9%
2 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7069
99.9%
None 2
 
< 0.1%
Misc Symbols 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 682
 
9.6%
e 644
 
9.1%
o 571
 
8.1%
r 509
 
7.2%
i 469
 
6.6%
n 378
 
5.3%
l 371
 
5.2%
t 319
 
4.5%
u 248
 
3.5%
s 220
 
3.1%
Other values (50) 2658
37.6%
None
ValueCountFrequency (%)
é 2
100.0%
Misc Symbols
ValueCountFrequency (%)
♀ 1
50.0%
♂ 1
50.0%

Type 1
Categorical

Distinct18
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size44.8 KiB
Water
112 
Normal
98 
Grass
70 
Bug
69 
Psychic
57 
Other values (13)
394 

Length

Max length8
Median length7
Mean length5.26
Min length3

Characters and Unicode

Total characters4208
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGrass
2nd rowGrass
3rd rowGrass
4th rowGrass
5th rowFire

Common Values

ValueCountFrequency (%)
Water 112
14.0%
Normal 98
12.2%
Grass 70
 
8.8%
Bug 69
 
8.6%
Psychic 57
 
7.1%
Fire 52
 
6.5%
Electric 44
 
5.5%
Rock 44
 
5.5%
Dragon 32
 
4.0%
Ground 32
 
4.0%
Other values (8) 190
23.8%

Length

2023-04-08T15:46:36.706246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
water 112
14.0%
normal 98
12.2%
grass 70
 
8.8%
bug 69
 
8.6%
psychic 57
 
7.1%
fire 52
 
6.5%
electric 44
 
5.5%
rock 44
 
5.5%
ghost 32
 
4.0%
ground 32
 
4.0%
Other values (8) 190
23.8%

Most occurring characters

ValueCountFrequency (%)
r 488
 
11.6%
a 360
 
8.6%
o 294
 
7.0%
e 286
 
6.8%
c 270
 
6.4%
s 257
 
6.1%
i 256
 
6.1%
t 242
 
5.8%
l 173
 
4.1%
g 159
 
3.8%
Other values (18) 1423
33.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3408
81.0%
Uppercase Letter 800
 
19.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 488
14.3%
a 360
10.6%
o 294
8.6%
e 286
8.4%
c 270
7.9%
s 257
7.5%
i 256
7.5%
t 242
 
7.1%
l 173
 
5.1%
g 159
 
4.7%
Other values (7) 623
18.3%
Uppercase Letter
ValueCountFrequency (%)
G 134
16.8%
W 112
14.0%
F 100
12.5%
N 98
12.2%
P 85
10.6%
B 69
8.6%
D 63
7.9%
E 44
 
5.5%
R 44
 
5.5%
S 27
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4208
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 488
 
11.6%
a 360
 
8.6%
o 294
 
7.0%
e 286
 
6.8%
c 270
 
6.4%
s 257
 
6.1%
i 256
 
6.1%
t 242
 
5.8%
l 173
 
4.1%
g 159
 
3.8%
Other values (18) 1423
33.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 488
 
11.6%
a 360
 
8.6%
o 294
 
7.0%
e 286
 
6.8%
c 270
 
6.4%
s 257
 
6.1%
i 256
 
6.1%
t 242
 
5.8%
l 173
 
4.1%
g 159
 
3.8%
Other values (18) 1423
33.8%

Type 2
Categorical

Distinct18
Distinct (%)4.3%
Missing386
Missing (%)48.2%
Memory size44.8 KiB
Flying
97 
Ground
35 
Poison
34 
Psychic
33 
Fighting
26 
Other values (13)
189 

Length

Max length8
Median length7
Mean length5.6521739
Min length3

Characters and Unicode

Total characters2340
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPoison
2nd rowPoison
3rd rowPoison
4th rowPoison
5th rowFlying

Common Values

ValueCountFrequency (%)
Flying 97
 
12.1%
Ground 35
 
4.4%
Poison 34
 
4.2%
Psychic 33
 
4.1%
Fighting 26
 
3.2%
Grass 25
 
3.1%
Fairy 23
 
2.9%
Steel 22
 
2.8%
Dark 20
 
2.5%
Dragon 18
 
2.2%
Other values (8) 81
 
10.1%
(Missing) 386
48.2%

Length

2023-04-08T15:46:36.845874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
flying 97
23.4%
ground 35
 
8.5%
poison 34
 
8.2%
psychic 33
 
8.0%
fighting 26
 
6.3%
grass 25
 
6.0%
fairy 23
 
5.6%
steel 22
 
5.3%
dark 20
 
4.8%
dragon 18
 
4.3%
Other values (8) 81
19.6%

Most occurring characters

ValueCountFrequency (%)
i 257
 
11.0%
n 210
 
9.0%
g 170
 
7.3%
F 158
 
6.8%
r 157
 
6.7%
y 153
 
6.5%
o 153
 
6.5%
s 131
 
5.6%
l 129
 
5.5%
c 106
 
4.5%
Other values (18) 716
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1926
82.3%
Uppercase Letter 414
 
17.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 257
13.3%
n 210
10.9%
g 170
8.8%
r 157
8.2%
y 153
7.9%
o 153
7.9%
s 131
 
6.8%
l 129
 
6.7%
c 106
 
5.5%
a 104
 
5.4%
Other values (7) 356
18.5%
Uppercase Letter
ValueCountFrequency (%)
F 158
38.2%
G 74
17.9%
P 67
16.2%
D 38
 
9.2%
S 22
 
5.3%
I 14
 
3.4%
R 14
 
3.4%
W 14
 
3.4%
E 6
 
1.4%
N 4
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2340
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 257
 
11.0%
n 210
 
9.0%
g 170
 
7.3%
F 158
 
6.8%
r 157
 
6.7%
y 153
 
6.5%
o 153
 
6.5%
s 131
 
5.6%
l 129
 
5.5%
c 106
 
4.5%
Other values (18) 716
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 257
 
11.0%
n 210
 
9.0%
g 170
 
7.3%
F 158
 
6.8%
r 157
 
6.7%
y 153
 
6.5%
o 153
 
6.5%
s 131
 
5.6%
l 129
 
5.5%
c 106
 
4.5%
Other values (18) 716
30.6%

Total
Real number (ℝ)

Distinct200
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean435.1025
Minimum180
Maximum780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:37.004449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile250
Q1330
median450
Q3515
95-th percentile630
Maximum780
Range600
Interquartile range (IQR)185

Descriptive statistics

Standard deviation119.96304
Coefficient of variation (CV)0.27571214
Kurtosis-0.50746071
Mean435.1025
Median Absolute Deviation (MAD)85
Skewness0.15252992
Sum348082
Variance14391.131
MonotonicityNot monotonic
2023-04-08T15:46:37.179574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 37
 
4.6%
405 26
 
3.2%
580 23
 
2.9%
500 23
 
2.9%
300 19
 
2.4%
490 18
 
2.2%
525 16
 
2.0%
495 15
 
1.9%
330 15
 
1.9%
480 15
 
1.9%
Other values (190) 593
74.1%
ValueCountFrequency (%)
180 1
 
0.1%
190 1
 
0.1%
194 1
 
0.1%
195 3
0.4%
198 1
 
0.1%
200 3
0.4%
205 5
0.6%
210 3
0.4%
213 1
 
0.1%
215 1
 
0.1%
ValueCountFrequency (%)
780 3
 
0.4%
770 2
 
0.2%
720 1
 
0.1%
700 9
1.1%
680 13
1.6%
670 4
 
0.5%
660 1
 
0.1%
640 1
 
0.1%
635 1
 
0.1%
634 2
 
0.2%

HP
Real number (ℝ)

Distinct94
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.25875
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:37.382839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35.95
Q150
median65
Q380
95-th percentile110
Maximum255
Range254
Interquartile range (IQR)30

Descriptive statistics

Standard deviation25.534669
Coefficient of variation (CV)0.3686851
Kurtosis7.2320784
Mean69.25875
Median Absolute Deviation (MAD)15
Skewness1.5682244
Sum55407
Variance652.01932
MonotonicityNot monotonic
2023-04-08T15:46:37.557553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 67
 
8.4%
50 63
 
7.9%
70 57
 
7.1%
65 46
 
5.8%
80 43
 
5.4%
75 43
 
5.4%
45 38
 
4.8%
40 38
 
4.8%
55 37
 
4.6%
100 32
 
4.0%
Other values (84) 336
42.0%
ValueCountFrequency (%)
1 1
 
0.1%
10 1
 
0.1%
20 6
 
0.8%
25 2
 
0.2%
28 1
 
0.1%
30 13
1.6%
31 1
 
0.1%
35 15
1.9%
36 1
 
0.1%
37 1
 
0.1%
ValueCountFrequency (%)
255 1
 
0.1%
250 1
 
0.1%
190 1
 
0.1%
170 1
 
0.1%
165 1
 
0.1%
160 1
 
0.1%
150 4
0.5%
144 1
 
0.1%
140 1
 
0.1%
135 1
 
0.1%

Attack
Real number (ℝ)

Distinct111
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.00125
Minimum5
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:37.728327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile30
Q155
median75
Q3100
95-th percentile136.2
Maximum190
Range185
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.457366
Coefficient of variation (CV)0.41084623
Kurtosis0.16971731
Mean79.00125
Median Absolute Deviation (MAD)20
Skewness0.55161375
Sum63201
Variance1053.4806
MonotonicityNot monotonic
2023-04-08T15:46:37.893069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 40
 
5.0%
65 39
 
4.9%
50 37
 
4.6%
80 37
 
4.6%
85 33
 
4.1%
60 33
 
4.1%
75 32
 
4.0%
70 31
 
3.9%
90 30
 
3.8%
55 30
 
3.8%
Other values (101) 458
57.2%
ValueCountFrequency (%)
5 2
 
0.2%
10 3
 
0.4%
15 1
 
0.1%
20 8
1.0%
22 1
 
0.1%
23 1
 
0.1%
24 1
 
0.1%
25 7
0.9%
27 1
 
0.1%
29 1
 
0.1%
ValueCountFrequency (%)
190 1
 
0.1%
185 1
 
0.1%
180 3
 
0.4%
170 2
 
0.2%
165 3
 
0.4%
164 1
 
0.1%
160 5
0.6%
155 2
 
0.2%
150 11
1.4%
147 1
 
0.1%

Defense
Real number (ℝ)

Distinct103
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.8425
Minimum5
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:38.073296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile35
Q150
median70
Q390
95-th percentile130
Maximum230
Range225
Interquartile range (IQR)40

Descriptive statistics

Standard deviation31.183501
Coefficient of variation (CV)0.42229747
Kurtosis2.7262604
Mean73.8425
Median Absolute Deviation (MAD)20
Skewness1.1559123
Sum59074
Variance972.41071
MonotonicityNot monotonic
2023-04-08T15:46:38.238574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 54
 
6.8%
50 49
 
6.1%
60 46
 
5.8%
80 39
 
4.9%
40 36
 
4.5%
65 36
 
4.5%
90 35
 
4.4%
100 33
 
4.1%
45 32
 
4.0%
55 32
 
4.0%
Other values (93) 408
51.0%
ValueCountFrequency (%)
5 2
 
0.2%
10 1
 
0.1%
15 4
 
0.5%
20 4
 
0.5%
23 1
 
0.1%
25 2
 
0.2%
28 1
 
0.1%
30 14
1.8%
32 2
 
0.2%
33 1
 
0.1%
ValueCountFrequency (%)
230 3
0.4%
200 2
 
0.2%
184 1
 
0.1%
180 3
0.4%
168 1
 
0.1%
160 3
0.4%
150 7
0.9%
145 2
 
0.2%
140 6
0.8%
135 2
 
0.2%

Sp. Atk
Real number (ℝ)

Distinct105
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.82
Minimum10
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:38.418255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile30
Q149.75
median65
Q395
95-th percentile131.05
Maximum194
Range184
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation32.722294
Coefficient of variation (CV)0.44935861
Kurtosis0.29789366
Mean72.82
Median Absolute Deviation (MAD)20
Skewness0.7446625
Sum58256
Variance1070.7485
MonotonicityNot monotonic
2023-04-08T15:46:38.588539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 51
 
6.4%
40 49
 
6.1%
65 44
 
5.5%
50 39
 
4.9%
55 35
 
4.4%
45 33
 
4.1%
70 30
 
3.8%
35 29
 
3.6%
85 27
 
3.4%
95 27
 
3.4%
Other values (95) 436
54.5%
ValueCountFrequency (%)
10 3
 
0.4%
15 4
 
0.5%
20 8
 
1.0%
23 1
 
0.1%
24 2
 
0.2%
25 11
1.4%
27 2
 
0.2%
29 1
 
0.1%
30 24
3.0%
31 1
 
0.1%
ValueCountFrequency (%)
194 1
 
0.1%
180 3
 
0.4%
175 1
 
0.1%
170 3
 
0.4%
165 2
 
0.2%
160 2
 
0.2%
159 1
 
0.1%
154 2
 
0.2%
150 9
1.1%
145 4
0.5%

Sp. Def
Real number (ℝ)

Distinct92
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.9025
Minimum20
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:38.769261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile32.95
Q150
median70
Q390
95-th percentile120
Maximum230
Range210
Interquartile range (IQR)40

Descriptive statistics

Standard deviation27.828916
Coefficient of variation (CV)0.38703683
Kurtosis1.6283941
Mean71.9025
Median Absolute Deviation (MAD)20
Skewness0.85401861
Sum57522
Variance774.44855
MonotonicityNot monotonic
2023-04-08T15:46:38.928270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 52
 
6.5%
50 50
 
6.2%
55 47
 
5.9%
65 44
 
5.5%
60 43
 
5.4%
70 40
 
5.0%
75 40
 
5.0%
90 36
 
4.5%
45 35
 
4.4%
40 30
 
3.8%
Other values (82) 383
47.9%
ValueCountFrequency (%)
20 6
 
0.8%
23 1
 
0.1%
25 11
1.4%
30 20
2.5%
31 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
34 1
 
0.1%
35 18
2.2%
36 1
 
0.1%
ValueCountFrequency (%)
230 1
 
0.1%
200 1
 
0.1%
160 2
 
0.2%
154 3
 
0.4%
150 7
0.9%
140 2
 
0.2%
138 1
 
0.1%
135 4
0.5%
130 9
1.1%
129 1
 
0.1%

Speed
Real number (ℝ)

Distinct108
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.2775
Minimum5
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:39.095392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile25
Q145
median65
Q390
95-th percentile115
Maximum180
Range175
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.060474
Coefficient of variation (CV)0.42562299
Kurtosis-0.23643667
Mean68.2775
Median Absolute Deviation (MAD)21
Skewness0.3579333
Sum54622
Variance844.51113
MonotonicityNot monotonic
2023-04-08T15:46:39.262128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 46
 
5.8%
60 44
 
5.5%
70 37
 
4.6%
65 36
 
4.5%
30 35
 
4.4%
80 33
 
4.1%
40 32
 
4.0%
90 31
 
3.9%
100 31
 
3.9%
55 30
 
3.8%
Other values (98) 445
55.6%
ValueCountFrequency (%)
5 2
 
0.2%
10 3
 
0.4%
15 9
1.1%
20 15
1.9%
22 1
 
0.1%
23 4
 
0.5%
24 1
 
0.1%
25 10
1.2%
28 4
 
0.5%
29 3
 
0.4%
ValueCountFrequency (%)
180 1
 
0.1%
160 1
 
0.1%
150 4
0.5%
145 3
0.4%
140 2
 
0.2%
135 2
 
0.2%
130 6
0.8%
128 1
 
0.1%
127 1
 
0.1%
126 1
 
0.1%

Generation
Real number (ℝ)

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.32375
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:39.400486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6612904
Coefficient of variation (CV)0.49982411
Kurtosis-1.2395758
Mean3.32375
Median Absolute Deviation (MAD)2
Skewness0.0142581
Sum2659
Variance2.7598858
MonotonicityIncreasing
2023-04-08T15:46:39.513179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 166
20.8%
5 165
20.6%
3 160
20.0%
4 121
15.1%
2 106
13.2%
6 82
10.2%
ValueCountFrequency (%)
1 166
20.8%
2 106
13.2%
3 160
20.0%
4 121
15.1%
5 165
20.6%
6 82
10.2%
ValueCountFrequency (%)
6 82
10.2%
5 165
20.6%
4 121
15.1%
3 160
20.0%
2 106
13.2%
1 166
20.8%

Legendary
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size39.3 KiB
False
735 
True
 
65
ValueCountFrequency (%)
False 735
91.9%
True 65
 
8.1%
2023-04-08T15:46:39.659566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Name_y
Real number (ℝ)

Distinct14
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.9875
Minimum4
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:39.781909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile24
Q132
median57
Q398
95-th percentile112
Maximum112
Range108
Interquartile range (IQR)66

Descriptive statistics

Standard deviation30.56481
Coefficient of variation (CV)0.50116515
Kurtosis-1.1335443
Mean60.9875
Median Absolute Deviation (MAD)25
Skewness0.42842061
Sum48790
Variance934.2076
MonotonicityNot monotonic
2023-04-08T15:46:39.908634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
112 112
14.0%
98 98
12.2%
32 96
12.0%
44 88
11.0%
70 70
8.8%
69 69
8.6%
57 57
7.1%
27 54
6.8%
52 52
6.5%
31 31
 
3.9%
Other values (4) 73
9.1%
ValueCountFrequency (%)
4 4
 
0.5%
17 17
 
2.1%
24 24
 
3.0%
27 54
6.8%
28 28
 
3.5%
31 31
 
3.9%
32 96
12.0%
44 88
11.0%
52 52
6.5%
57 57
7.1%
ValueCountFrequency (%)
112 112
14.0%
98 98
12.2%
70 70
8.8%
69 69
8.6%
57 57
7.1%
52 52
6.5%
44 88
11.0%
32 96
12.0%
31 31
 
3.9%
28 28
 
3.5%

percentage
Real number (ℝ)

Distinct14
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6234375
Minimum0.5
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.8 KiB
2023-04-08T15:46:40.029989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile3
Q14
median7.125
Q312.25
95-th percentile14
Maximum14
Range13.5
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation3.8206012
Coefficient of variation (CV)0.50116515
Kurtosis-1.1335443
Mean7.6234375
Median Absolute Deviation (MAD)3.125
Skewness0.42842061
Sum6098.75
Variance14.596994
MonotonicityNot monotonic
2023-04-08T15:46:40.161818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
14 112
14.0%
12.25 98
12.2%
4 96
12.0%
5.5 88
11.0%
8.75 70
8.8%
8.625 69
8.6%
7.125 57
7.1%
3.375 54
6.8%
6.5 52
6.5%
3.875 31
 
3.9%
Other values (4) 73
9.1%
ValueCountFrequency (%)
0.5 4
 
0.5%
2.125 17
 
2.1%
3 24
 
3.0%
3.375 54
6.8%
3.5 28
 
3.5%
3.875 31
 
3.9%
4 96
12.0%
5.5 88
11.0%
6.5 52
6.5%
7.125 57
7.1%
ValueCountFrequency (%)
14 112
14.0%
12.25 98
12.2%
8.75 70
8.8%
8.625 69
8.6%
7.125 57
7.1%
6.5 52
6.5%
5.5 88
11.0%
4 96
12.0%
3.875 31
 
3.9%
3.5 28
 
3.5%

Segment
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size44.8 KiB
0.0
748 
1.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2400
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 748
93.5%
1.0 52
 
6.5%

Length

2023-04-08T15:46:40.292015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T15:46:40.417447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 748
93.5%
1.0 52
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 1548
64.5%
. 800
33.3%
1 52
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1600
66.7%
Other Punctuation 800
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1548
96.8%
1 52
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1548
64.5%
. 800
33.3%
1 52
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1548
64.5%
. 800
33.3%
1 52
 
2.2%

Interactions

2023-04-08T15:46:33.685375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:16.736047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:18.459677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:20.169598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:21.986979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:23.684184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:25.294081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:26.968839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:28.706201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:30.412579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:32.053727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:33.845470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:16.911379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:18.617254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:20.325041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:22.144455image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:23.835654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:25.448353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:27.113861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:28.863758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:30.566773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:32.208178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:34.000022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:17.068472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:18.776976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:20.473341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:22.299939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:23.983990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:25.602093image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:27.258884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:29.019404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:30.718488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:32.361202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:34.148118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:17.220388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:18.929885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:20.619964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:22.449503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:24.142668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:25.751838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:27.578562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:29.167396image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:30.867260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:32.504919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:34.306891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:17.378000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:19.094344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:20.775731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:22.609773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:24.292986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:25.907917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:27.726052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:29.328604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:31.022187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:32.666431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:34.450203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:17.524511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:19.245733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:20.935394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:22.750911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:24.430152image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:26.050398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:27.858474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:29.471488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:31.158919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:32.808003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:34.607222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:17.685037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:19.404101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:21.095028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:22.912057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:24.582436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:26.209589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:28.004478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:29.640700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:31.318788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:32.961176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:34.746739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:17.829449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:19.546465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:21.377965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:23.054352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:24.714113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:26.351005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:28.134814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:29.780200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:31.453868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:33.095737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:34.903615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:17.995431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:19.707621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:21.535611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:23.214652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:24.862807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:26.507234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:28.282617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:29.945483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:31.606218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:33.245851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:35.271088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:18.147123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:19.858466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:21.684577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:23.366166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:25.005068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:26.658766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:28.422555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:30.101336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:31.753376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:33.390126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:35.419077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:18.298115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:20.011263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:21.830653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:23.518898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:25.144625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:26.812067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:28.559407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:30.253615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:31.898955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-08T15:46:33.532240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-04-08T15:46:40.538065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
#TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationName_ypercentageType 1Type 2LegendarySegment
#1.0000.1220.1200.1030.1170.0880.0760.0190.984-0.191-0.1910.1620.2650.2590.074
Total0.1221.0000.7130.7200.6820.7300.7570.5680.054-0.101-0.1010.1290.1190.7640.103
HP0.1200.7131.0000.5660.4330.4710.4930.2660.0820.0140.0140.0770.1440.3570.000
Attack0.1030.7200.5661.0000.5150.3620.3210.3730.054-0.144-0.1440.1190.1050.3660.000
Defense0.1170.6820.4330.5151.0000.3140.5790.0930.058-0.144-0.1440.1510.1470.2740.000
Sp. Atk0.0880.7300.4710.3620.3141.0000.5720.4600.039-0.036-0.0360.1490.0700.5020.137
Sp. Def0.0760.7570.4930.3210.5790.5721.0000.3210.019-0.082-0.0820.0820.0970.3870.024
Speed0.0190.5680.2660.3730.0930.4600.3211.000-0.0140.0170.0170.1300.1390.3420.091
Generation0.9840.0540.0820.0540.0580.0390.019-0.0141.000-0.154-0.1540.1580.2820.0780.000
Name_y-0.191-0.1010.014-0.144-0.144-0.036-0.0820.017-0.1541.0001.0000.9940.2750.2030.658
percentage-0.191-0.1010.014-0.144-0.144-0.036-0.0820.017-0.1541.0001.0000.9940.2750.2030.658
Type 10.1620.1290.0770.1190.1510.1490.0820.1300.1580.9940.9941.0000.2440.3030.990
Type 20.2650.1190.1440.1050.1470.0700.0970.1390.2820.2750.2750.2441.0000.1350.263
Legendary0.2590.7640.3570.3660.2740.5020.3870.3420.0780.2030.2030.3030.1351.0000.000
Segment0.0740.1030.0000.0000.0000.1370.0240.0910.0000.6580.6580.9900.2630.0001.000

Missing values

2023-04-08T15:46:35.655332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-08T15:46:35.936473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

#Name_xType 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendaryName_ypercentageSegment
01BulbasaurGrassPoison3184549496565451False708.750.0
12IvysaurGrassPoison4056062638080601False708.750.0
23VenusaurGrassPoison525808283100100801False708.750.0
33VenusaurMega VenusaurGrassPoison62580100123122120801False708.750.0
44CharmanderFireNaN3093952436050651False526.501.0
55CharmeleonFireNaN4055864588065801False526.501.0
66CharizardFireFlying534788478109851001False526.501.0
76CharizardMega Charizard XFireDragon63478130111130851001False526.501.0
86CharizardMega Charizard YFireFlying63478104781591151001False526.501.0
97SquirtleWaterNaN3144448655064431False11214.000.0
#Name_xType 1Type 2TotalHPAttackDefenseSp. AtkSp. DefSpeedGenerationLegendaryName_ypercentageSegment
790714NoibatFlyingDragon2454030354540556False40.5000.0
791715NoivernFlyingDragon53585708097801236False40.5000.0
792716XerneasFairyNaN6801261319513198996True172.1250.0
793717YveltalDarkFlying6801261319513198996True313.8750.0
794718Zygarde50% FormeDragonGround6001081001218195956True324.0000.0
795719DiancieRockFairy60050100150100150506True445.5000.0
796719DiancieMega DiancieRockFairy700501601101601101106True445.5000.0
797720HoopaHoopa ConfinedPsychicGhost6008011060150130706True577.1250.0
798720HoopaHoopa UnboundPsychicDark6808016060170130806True577.1250.0
799721VolcanionFireWater6008011012013090706True526.5001.0